本文關(guān)鍵詞： 認(rèn)知雷達(dá) 目標(biāo)檢測(cè) 目標(biāo)跟蹤 閉環(huán)反饋 波形自適應(yīng)　出處：《大連海事大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：認(rèn)知雷達(dá)通過(guò)引入接收機(jī)到發(fā)射機(jī)的閉環(huán)反饋使雷達(dá)具有感知環(huán)境與判斷決策的能力,從而在當(dāng)今復(fù)雜多變的電磁環(huán)境下更加游刃有余的完成相應(yīng)任務(wù)。認(rèn)知雷達(dá)中有兩項(xiàng)關(guān)鍵技術(shù):波形最優(yōu)化技術(shù)與自適應(yīng)機(jī)制。本文著重探討這兩項(xiàng)關(guān)鍵技術(shù)在檢測(cè)與跟蹤兩大典型雷達(dá)任務(wù)中的應(yīng)用。論文主要工作如下:(1)論述了認(rèn)知雷達(dá)目標(biāo)檢測(cè)識(shí)別的波形最優(yōu)化方法,在統(tǒng)計(jì)假設(shè)檢驗(yàn)理論基礎(chǔ)上建立目標(biāo)響應(yīng)模型與雜波環(huán)境下的收發(fā)模型,并基于奈曼皮爾遜準(zhǔn)則構(gòu)建最優(yōu)波形的目標(biāo)函數(shù)以求解最優(yōu)波形。研究了單目標(biāo)跟蹤的最優(yōu)發(fā)射波形選擇算法,闡述發(fā)射波形參數(shù)同卡爾曼濾波中測(cè)量噪聲協(xié)方差的聯(lián)系,并分別利用最小均方誤差準(zhǔn)則和最大互信息準(zhǔn)則進(jìn)行波形參數(shù)選擇。(2)綜合采用了雷達(dá)發(fā)射機(jī)-接收機(jī)閉環(huán)反饋和接收機(jī)內(nèi)部檢測(cè)器-跟蹤器閉環(huán)反饋兩種自適應(yīng)機(jī)制,建立了雜波環(huán)境下單目標(biāo)認(rèn)知的PDA跟蹤算法。在發(fā)射機(jī)-接收機(jī)的閉環(huán)反饋中,將波形選擇模塊引入傳統(tǒng)概率數(shù)據(jù)關(guān)聯(lián)算法,提高了目標(biāo)的跟蹤精度;在接收機(jī)內(nèi)部檢測(cè)器-跟蹤器閉環(huán)反饋中,進(jìn)行檢測(cè)與跟蹤的綜合處理,并引入目標(biāo)的幅值和位置聯(lián)合信息對(duì)關(guān)聯(lián)概率進(jìn)行修正,與傳統(tǒng)算法相比提高了目標(biāo)檢測(cè)性能和跟蹤精度。(3)在單發(fā)射波束下將波形選擇模塊引入傳統(tǒng)聯(lián)合概率數(shù)據(jù)關(guān)聯(lián)算法中,建立了雜波環(huán)境下多目標(biāo)認(rèn)知的JPDA跟蹤算法。仿真表明,對(duì)存在航跡交叉的運(yùn)動(dòng)目標(biāo),與傳統(tǒng)算法相比跟蹤精度得到提高。建立了多目標(biāo)跟蹤的多發(fā)射波束有限功率資源的智能化分配算法,提高了最差目標(biāo)的跟蹤精度。(4)提出一種自適應(yīng)機(jī)動(dòng)目標(biāo)認(rèn)知跟蹤算法,在變結(jié)構(gòu)交互多模型算法基礎(chǔ)上采用一種波門自適應(yīng)調(diào)節(jié)方法以處理因目標(biāo)機(jī)動(dòng)導(dǎo)致的跟丟率驟增情況,同時(shí)引入波形選擇模塊,使跟蹤誤差逼近估計(jì)量的克拉美羅下界,并仿真驗(yàn)證了波形選擇算法的魯棒性。
[Abstract]:By introducing closed-loop feedback from receiver to transmitter, cognitive radar has the ability to perceive environment and judge decision. There are two key technologies in cognitive radar: waveform optimization technology and adaptive mechanism. This paper focuses on these two key technologies in the field of cognitive radar. Application of two typical radar tasks: detection and tracking. The main work of this paper is as follows: 1) the waveform optimization method for target detection and recognition of cognitive radar is discussed. Based on the statistical hypothesis test theory, the target response model and the transceiver model in clutter environment are established. The objective function of the optimal waveform is constructed based on the Nyman Pearson criterion to solve the optimal waveform. The optimal waveform selection algorithm for single target tracking is studied. The relationship between the parameters of the transmitting waveform and the measurement noise covariance in Kalman filter is discussed. The minimum mean square error criterion and the maximum mutual information criterion are used to select the waveform parameters respectively. The two adaptive mechanisms of radar transmitter receiver closed-loop feedback and receiver internal detector tracker closed-loop feedback are adopted respectively. The PDA tracking algorithm for target recognition in clutter environment is established. In the closed-loop feedback between transmitter and receiver, the waveform selection module is introduced into the traditional probabilistic data association algorithm to improve the tracking accuracy of the target. In the closed-loop feedback of the receiver's internal detector and tracker, the detection and tracking are integrated, and the associated probability is corrected by introducing the joint information of the target amplitude and position. Compared with the traditional algorithm, the target detection performance and tracking accuracy are improved. (3) the waveform selection module is introduced into the traditional joint probabilistic data association algorithm under single transmit beam, and a multi-target cognitive JPDA tracking algorithm in clutter environment is established. Compared with the traditional algorithm, the tracking accuracy of moving targets with track crossing is improved. An intelligent allocation algorithm for multi-beam finite power resources with multi-target tracking is established. An adaptive maneuvering target cognitive tracking algorithm is proposed. Based on the variable structure interactive multi-model algorithm, a wave gate adaptive adjustment method is adopted to deal with the sudden increase in the loss rate caused by target maneuvering. At the same time, the waveform selection module is introduced to make the tracking error approach the lower bound of the estimator, and the robustness of the waveform selection algorithm is verified by simulation.
【學(xué)位授予單位】：大連海事大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN958

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